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access icon free Machine-learning methodology for energy efficient routing

© The Institution of Engineering and Technology
Received 11/01/2013, Accepted 01/06/2013, Revised 31/05/2013, Published 16/08/2013

Eco-driving assistance systems encourage economical driving behaviour and support the driver in optimising his/her driving style to achieve fuel economy and consequently, emission reductions. Energy efficiency is also one of the most pertinent issues related to the autonomy of fully electric vehicles. This study introduces a novel methodology for energy efficient routing, based on the realisation of dependable energy consumption predictions for the various road segments constituting an actual or potential vehicle route, performed mainly by means of machine-learning functionality. This proposed innovative methodology, the functional architecture implementing it, as well as demonstrative experimental results are presented in this study.

Inspec keywords: energy conservation; learning (artificial intelligence); road vehicles; electric vehicles; road traffic; electrical engineering computing; traffic engineering computing

Other keywords: emission reductions; energy efficient routing; electric vehicles; ecodriving assistance systems; fuel economy; road segments; economical driving behaviour; machine learning methodology; vehicle route; energy consumption predictions; machine learning functionality

Subjects: Electrical engineering computing; Knowledge engineering techniques; Energy conservation; Transportation; Traffic engineering computing

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
      • 4. Rakha, H., Kamalanathsharma, R.K.: ‘Eco-driving at signalized intersections using V2I communication’. Proc. 14th Int. IEEE Conf. Intelligent Transportation Systems (ITSC), October 2011, pp. 341–346.
    11. 11)
      • 13. Frey, H.C., Zhang, K., Rouphail, N.M.: ‘Fuel use and emissions comparisons for alternative routes, time of day, road grade, and vehicles based on in-use measurements’, J. Environ. Sci. Technol., 2008, 42, (7), pp. 2483–2489 (doi: 10.1021/es702493v).
    12. 12)
      • 5. Ericsson, E., Larsson, H., Brundell-Freij, K.: ‘Optimizing route choice for lowest fuel consumption – potential effects of a new driver support tool’, J. Transp. Res. C, Emerging Technol., 2006, 14, (6), pp. 369–383 (doi: 10.1016/j.trc.2006.10.001).
    13. 13)
      • 10. Barth, M., Boriboonsomsin, K., Vu, A.: ‘Environmentally-friendly navigation’. Proc. IEEE Intelligent Transportation Systems Conf., Seattle, September 2007, pp. 684–689.
    14. 14)
      • 26. NOKIA here Website, available at http://www.here.com, accessed December 2012.
    15. 15)
      • 19. Ki, Y.-K.: ‘Speed-measurement model utilising embedded triple-loop sensors’, IET Intell. Transp. Syst., 2011, 5, (1), pp. 32–37 (doi: 10.1049/iet-its.2008.0084).
    16. 16)
      • 12. Boriboonsomsin, K., Barth, M.J., Zhu, W., Vu, A.: ‘Eco-routing navigation system based on multisource historical and real-time traffic information’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (4), pp. 1694–1704 (doi: 10.1109/TITS.2012.2204051).
    17. 17)
      • 28. EcoGem Project Official Website, available at http://www.ecogem.eu, accessed December 2012.
    18. 18)
      • 21. OICA Definition Test Procedure WLTP-DTP-02–04, V.: 28.05.2010.
    19. 19)
      • 11. Kraschl-Hirschmann, K., Fellendorf, M.: ‘Estimating energy consumption for routing algorithms’. Proc. IEEE Intelligent Vehicles Symp. (IV), June 2012, pp. 258–263.
    20. 20)
      • 7. Mitchell, T.: ‘Machine learning’ (McGraw-Hill, 2002).
    21. 21)
      • 23. CANcaseXL product Webpage, available at http://www.vector.com/vi_cancase_xl_log_en.html, accessed December 2012.
    22. 22)
      • 16. Bandeira, J., Carvalho, D., Khattak, A., Coelho, M.C.: ‘A comparative empirical analysis of eco-friendly routes during peak and off-peak hours’. Proc. 91st Annual Meeting of the Transportation Research Board, Washington, DC, 2012.
    23. 23)
      • 2. Kamal, M.A.S., Mukai, M., Murata, J., Kawabe, T.: ‘Ecological vehicle control on roads with up-down slopes’, IEEE Trans. Intell. Transp. Syst., 2011, 12, (3), pp. 783–794 (doi: 10.1109/TITS.2011.2112648).
    24. 24)
      • 25. Lawrence, S., Giles, C.L.: ‘Overfitting and neural networks: conjugate gradient and backpropagation’. Proc. Int. Joint Conf. Neural Networks, 2000, vol. 1, pp. 114–119.
    25. 25)
      • 9. Kono, T., Fushiki, T., Asada, K., Nakano, K.: ‘Fuel consumption analysis and prediction model for “eco” route search’. Proc. 15th World Congress on Intelligent Transport Systems and ITS America's 2008 Annual Meeting, New York, November 2008.
    26. 26)
      • 20. EcoGem Project, Deliverable D3.1, ‘Scalability and Performance Analysis of EcoGem Applications’, June 2011.
    27. 27)
      • 22. THE Open Group: ‘ArchiMate 2.0 Specification’ (Van Haren Publishing, 2012).
    28. 28)
      • 14. Ahn, K., Rakha, H.: ‘The effects of route choice decisions on vehicle energy consumption and emissions’, Transp. Res. D, Transp. Environ., 2008, 13, (3), pp. 151–167 (doi: 10.1016/j.trd.2008.01.005).
    29. 29)
      • 24. Caruana, R., Niculescu-Mizil, Al.: ‘An empirical comparison of supervised learning algorithms’. Proc. 23rd Int. Conf. Machine learning, New York, USA, 2006, pp. 161–168.
    30. 30)
      • 15. Vlieger, I.De., Keukeleere, D.De., Kretzschmar, J.G.: ‘Environmental effects of driving behaviour and congestion related to passenger cars’, Atmos. Environ., 2000, 34, (27), pp. 4649–4655 (doi: 10.1016/S1352-2310(00)00217-X).
    31. 31)
      • 27. http://www.statistics.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_ RSquareds.htm, accessed April 2013.
    32. 32)
      • 6. Trommer, S., Höltl, A.: ‘Perceived usefulness of eco-driving assistance systems in Europe’, IET Intell. Transp. Syst., 2012, 6, (2), pp. 145–152 (doi: 10.1049/iet-its.2011.0154).
    33. 33)
      • 8. Minett, C.F., Salomons, A.M., Daamen, W., van Arem, B., Kuijpers, S.: ‘Eco-routing: comparing the fuel consumption of different routes between an origin and destination using field test speed profiles and synthetic speed profiles’. Proc. IEEE Forum on Integrated and Sustainable Transportation System (FISTS), Vienna, Austria, June 2011, pp. 32–39.
    34. 34)
      • 3. Yuxiao, C., Dezhao, Z., Keqiang, L.: ‘Enhanced eco-driving system based on V2X communication’. Proc. 15th Int. IEEE Conf. Intelligent Transportation Systems (ITSC), September 2012, pp. 200–205.
    35. 35)
      • 18. Jianming, H., Qiang, M., Qi, W., Jiajie, Z., Yi, Z.: ‘Traffic congestion identification based on image processing’, IET Intell. Transp. Syst., 2012, 6, (2), pp. 153–160 (doi: 10.1049/iet-its.2011.0124).
    36. 36)
      • 1. Wada, T., Yoshimura, K., Doi, S.-I., Youhata, H., Tomiyama, K.: ‘Proposal of an eco-driving assist system adaptive to driver's skill’. Proc. 14th Int. IEEE Conf. Intelligent Transportation Systems (ITSC), October 2011, pp. 1880–1885.
    37. 37)
      • 17. Ben Dhaou, I.: ‘Fuel estimation model for ECO-driving and ECO-routing’. Proc. IEEE Intelligent Vehicles Symp. (IV), June 2011, pp. 37–42.
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